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Computer Simulation of the Phase Transitions in Three-Dimensional Weakly Diluted Spin Systems

JOURNAL OF SURFACE INVESTIGATION 2022年第2期16卷 221-225页

作者： Murtazaev, A. K. Babaev, A. B. Russian Acad Sci Amirkhanov Inst Phys Dagestan Fed Res Ctr Makhachkala 367010 Russia Russian Acad Sci Dagestan Fed Res Ctr Makhachkala 367032 Russia

A computer simulation of the phase transitions in a three-dimensional weakly diluted five-state Potts model on a simple cubic lattice is performed. Calculations are carried out for spin systems with periodic boundary conditions at spin concentrations of p = 1.0 and p = 0.90. Systems with linear dimensions L x L x L = N, L = 10-80 are considered. The temperature dependences of the specific heat, susceptibility, and magnetization as a function of the linear dimensions of the studied systems are obtained. Using the fourth-order Binder cumulants and using histogram-data analysis, it is shown that the presence of weak disorder in the form of nonmagnetic impurities of the order c = 10% (c = 1 - p, p is the spin concentration) does not affect the phase transition of the first-order.

关键词： disorder defects Potts model cubic lattice cluster algorithm Monte Carlo method computer simulation phase transition Binder cumulants histogram energy distribution

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Using a Self-clustering algorithm and Type-2 Fuzzy Controller for Multi-robot Deployment and Navigation in Dynamic Environments

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ASIAN JOURNAL OF CONTROL 2020年第6期22卷 2143-2155页

作者： Jhang, Jyun-Yu Lee, Chin-Ling Lin, Cheng-Jian Young, Kuu-Young Natl Chiao Tung Univ Inst Elect & Control Engn Hsinchu 300 Taiwan Natl Taichung Univ Sci & Technol Dept Int Business Taichung 404 Taiwan Natl Chin Yi Univ Technol Dept Comp Sci & Informat Engn Taichung 406 Taiwan

This study proposes a novel method for multi-robot deployment and navigation under dynamic environments. To automatically determine the location deployment of multiple robots, a grid-based method and self-clustering algorithm (SCA) were used to simplify the environmental information and automatically deploy robot locations. In the navigation process, a behavior selector automatically turns on towards goal mode or wall-following mode (WFM) depending on environmental conditions. WFM control adopts an interval type-2 fuzzy controller (IT2FC). The parameters of the IT2FC are adjusted by using the dynamic group whale optimization algorithm (DGWOA). The proposed DGWOA uses a dynamic group and Levy flight strategy to overcome the problem of falling into a local minimum solution. Experimental results reveal that the proposed method can successfully complete navigation tasks under dynamic environments.

关键词： cluster algorithm fuzzy controller mobile robot control navigation control robot deployment type-2 fuzzy set

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Research on Network Simplification by Edge Bundling 1

Research on Network Simplification by Edge Bundling

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1st IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Yao Zhonghua Wu Lingda Equipment Acad Sci & Technol Complex Elect Syst Simulat Lab Beijing Peoples R China

ISBN: (纸本)9781509011926

Node occlusion and edge congestion problems, which are caused by the increasing scale and complexity of network, had become a hot spot in network visualization research. To solve the visual clutter problem in network, edges close to each other in network were bundled by curving them. A segmental forced-directed algorithm (FDA) simplification model and a community based compatible edge bundling network model were proposed and improved. To solve the problem of excessive bending of some edges in segmental FDA bundling model, network was divided into different communities by CNM cluster algorithm. Experimental result shows that the bundling simplification algorithm introduced above has a wide applicability, and network visualized by this algorithm has good visual effect and readability.

关键词： network visualization edge bundling cluster algorithm segmental force-directed algorithm simplification

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Three Dimensional Reconstruction of Die Design Based on clustering algorithm and Fuzzy Control

Three Dimensional Reconstruction of Die Design Based on Clus...

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International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Liu, Han Jiangxi Univ Finance & Econ Dept Art Design Coll Art Nanchang 330013 Peoples R China

ISBN: (纸本)9783038351153

Based on the fuzzy control cluster algorithm, we establish the modern charm mathematical model of traditional toys with UG software, and design the 3D reconstruction system of traditional folk toys through program. In order to verify the effectiveness of the system, we use the UG software to do 3D reconstruction simulation on the modern charm of traditional toys, and obtain the 3D key point capture graph. Through the key point we construct 3D key line of toys. From the statistics of performance, the maximum frequency of key point reconstruction is up to 8500 times /s, which has good performance. Finally, through the UG 3D reconstruction we get the modern fashion reconstruction design for traditional toy, which provides the technical reference for the study of traditional folk toys.

关键词： Fuzzy control cluster algorithm Tradition characteristics Folk toys UG software 3D reconstruction

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Topic Detection in Twitter Based on Label Propagation Model 13

Topic Detection in Twitter Based on Label Propagation Model

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13th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES)

作者： Huang, Dongxu Mu, Dejun Northwest Polytech Univ Sch Automat Xian Peoples R China

ISBN: (纸本)9781479941698

Many kinds of huge amount of tweets about real-world events are generated everyday in Twitter. However, the disorganization messages required to be classified by topics and events are one of challenges to get knowledge effectively. To solve the problem, we propose a novel method that combines the cluster algorithm with label propagation algorithm to detect topics in twitter. First, we use canopy cluster algorithm to cluster tweets, canopy cluster algorithm could divides a tweet into different clusters, and the tweet which only belongs to one cluster will be labeled. Second, the mechanism of label propagation is used to label the tweets that in the overlapping of different clusters. In order to evaluate our algorithm, we use two baseline algorithms, LDA (Latent Dirichlet Allocation) and Single-Pass cluster algorithm. We apply three algorithms on tweet dataset with three topics and some noisy data, and experiment results show our method outperforms other algorithms on precision and recall rate.

关键词： topic detection twitter cluster algorithm label propagation model

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The Exploration and Realization of Clothing Sizes Support System Based on Data Mining 5

The Exploration and Realization of Clothing Sizes Support Sy...

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Fifth International Conference on Instrumentation & Measurement, Computer, Communication, and Control (IMCCC)

作者： RenRong North Nationality Univ Comp Sci & Engn Inst Yinchuan Peoples R China

ISBN: (纸本)9781467377232

The research and application of data warehouse and data mining technology is the core in this paper. The design and implementation of "The productive apparel sizes and styles auxiliary decision system" is the main line. The effect of productive sizes support system is to guide the sizes quantity and enhance production efficiency based on the result of sizes compression and sales trend analysis.

关键词： Productive Decision cluster algorithm Time-Series algorithm Clothing Sizes and Styles SQL Server2005

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HTTP-sCAN: detecting HTTP-flooding attaCk by modeling multi-feAtures of web browsing behavior from Noisy dataset

HTTP-sCAN: detecting HTTP-flooding attaCk by modeling multi-...

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19th Asia-Pacific Conference on Communications (APCC)

作者： Wang, Jin Zhang, Min Yang, Xiaolong Long, Keping Zhou, Chimin Uni Sci & Technol Beijing Sch Comp & Commun Engn Beijing Peoples R China Chengdu Univ Network Ctr Chengdu 610106 Peoples R China Sichuan Radio & TV Univ Ctr Informat Technol Chengdu 610073 Peoples R China

ISBN: (纸本)9781467360500

HTTP-flooding attack disables the victimized web server by sending a large number of HTTP Get requests. Recent research tends to detect the attacks with the anomaly-based approaches, which detect the HTTP-flooding by modeling the behavior of normal web users. However, most of the existing anomaly-based detection approaches usually cannot filter the web crawling traces of the unknown search bots mixed in the normal web browsing logs. These web-crawling traces can bias the detection model in the training phase, thus further influencing the performance of the anomaly-based detection schemes. This paper proposes a novel anomaly-based HTTP-flooding detection scheme (HTTP-sCAN), which can eliminate the influence of the web-crawling traces with the cluster algorithm. The simulation results show that HTTP-sCAN is immune to the interferences of unknown search sessions, and can detect all HTTP-flooding attacks.

关键词： IP network DDoS Relative Entropy cluster algorithm

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Fast SVM Incremental Learning Based on clustering algorithm

Fast SVM Incremental Learning Based on Clustering Algorithm

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IEEE International Conference on Intelligent Computing and Intelligent Systems

作者： Du Hongle Teng Shaohua Zhu Qingfang Guangdong Univ Technol Fac Comp Guangzhou Guangdong Peoples R China Luoyang Normal Univ Coll Math Luoyang Peoples R China

ISBN: (纸本)9781424447541

In the incremental learning process of Support Vector Machines, the Non-support sectors which is close to support vector samples are discarded in tradition method But it is likely to change into the Support Vector after adding new training samples To resolve this problem, this paper proposes a new method that combines Support Vector Machine with clustering algorithm In this method, firstly, use clustering algorithm to cluster the training sample set and get clustering particles, secondly, look all centers of clustering particles as new samples training set and reconstruct the training samples set, then, train the nos training samples set with Fuzzy Support Vector Machine (FSVM) and obtain the support vectors, and discard the samples that satisfy KKT conditions, put the samples that don not meet the KKT conditions and the support vectors together to reconstitute a new training set, train them again Experimental results shim that this method can enhance the classification accuracy rate and improve the speed of SVM training and classification speed, as keeping the generalization ability of SVM incremental learning

关键词： Support vector machine Incremental learning cluster algorithm KKT condition

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Applied Research on Data Mining algorithm in Network Intrusion Detection

Applied Research on Data Mining Algorithm in Network Intrusi...

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1st IITA International Joint Conference on Artificial Intelligence

作者： Xue, Ming Zhu, Changjun Changchun Inst Technol Changchun 130012 Peoples R China HeBei Univ Engn Handan 056038 Peoples R China

ISBN: (纸本)9780769536156

Intrusion Detection is one of network security area of technology main research directions. Data mining technology will be applied to Network Intrusion Detection System (NIDS), may automatically discover the new pattern from the massive network data, to reduce the workload of the manual compilation intrusion behavior patterns and normal behavior patterns. This article reviewed the current intrusion detection technology and the data mining technology briefly. Focus on data mining algorithm in anomaly detection and misuse detection of specific applications. For misuse detection, the main study the classification algorithm;For anomaly detection, the main study the pattern comparison and the cluster algorithm. In pattern comparison to analysis deeply the association rules and sequence rules. Finally, has analysised the difficulties which the current data mining algorithm in intrusion detection applications faced at present, and has indicated the next research direction.

关键词： intrusion detection data mining classification algorithm association rules sequence rules cluster algorithm

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Improved EM Method for Internet Traffic Classification 8

Improved EM Method for Internet Traffic Classification

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8th International Conference on Knowledge and Smart Technology (KST 206)

作者： Liu, Songyin Hu, Jing Hao, Shengnan Song, Tiecheng Southeast Univ Natl Mobile Commun Res Lab Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781467381390

Network traffic classification algorithm based on the machine learning has attracted more and more attention. Because the traditional EM algorithm has the disadvantage that the algorithm has the sensitivity of initial value and converge to local optimal point easily. This paper proposed a new improved EM algorithm based on the q-DAEM. The improved algorithm applies the EM algorithm to generate a constrained matrix, then combine the constrained matrix with the q-DAEM algorithm to reduce the search range, so that a better Gaussian mixture model can he derived from this algorithm. The algorithm was applied to the Moore datasets for evaluation, the experimental results show that this improved algorithm which applied to network traffic classification can lead to a higher precision and overall accuracy.

关键词： Expectation Maximization Gaussian mixture model Network traffic classification cluster algorithm

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